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HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

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HAPRAP : a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics. / Zheng, Jie; Rodriguez, Santi; Laurin, Charles; Baird, Denis; Trela-Larsen, Lea; Erzurumluoglu, Mesut; Zheng, Yi; White, Jon; Giambartolomei, Claudia; Zabaneh, Delilah; Morris, Richard; Kumari, Meena; Casas, Juan-Pablo; Hingorani, Aroon D; Evans, David; Gaunt, Tom; Day, Ian.

In: Bioinformatics, Vol. 33, No. 1, 01.2017, p. 79-86.

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Zheng, J, Rodriguez, S, Laurin, C, Baird, D, Trela-Larsen, L, Erzurumluoglu, M, Zheng, Y, White, J, Giambartolomei, C, Zabaneh, D, Morris, R, Kumari, M, Casas, J-P, Hingorani, AD, Evans, D, Gaunt, T & Day, I 2017, 'HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics', Bioinformatics, vol. 33, no. 1, pp. 79-86. https://doi.org/10.1093/bioinformatics/btw565

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Zheng, Jie ; Rodriguez, Santi ; Laurin, Charles ; Baird, Denis ; Trela-Larsen, Lea ; Erzurumluoglu, Mesut ; Zheng, Yi ; White, Jon ; Giambartolomei, Claudia ; Zabaneh, Delilah ; Morris, Richard ; Kumari, Meena ; Casas, Juan-Pablo ; Hingorani, Aroon D ; Evans, David ; Gaunt, Tom ; Day, Ian. / HAPRAP : a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics. In: Bioinformatics. 2017 ; Vol. 33, No. 1. pp. 79-86.

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@article{ac9184cc02d5454a91a0f922422d50a0,
title = "HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics",
abstract = "Motivation: Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r2) of the variants. However, haplotypes rather than pairwise r2, are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this paper, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.Results: Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits (GIANT) height data, HAPRAP performs well with a small training sample size (N<2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by SNPs with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).Availability: The HAPRAP package and documentation are available online: http://apps.biocompute.org.uk/haprap/",
author = "Jie Zheng and Santi Rodriguez and Charles Laurin and Denis Baird and Lea Trela-Larsen and Mesut Erzurumluoglu and Yi Zheng and Jon White and Claudia Giambartolomei and Delilah Zabaneh and Richard Morris and Meena Kumari and Juan-Pablo Casas and Hingorani, {Aroon D} and David Evans and Tom Gaunt and Ian Day",
year = "2017",
month = "1",
doi = "10.1093/bioinformatics/btw565",
language = "English",
volume = "33",
pages = "79--86",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "1",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - HAPRAP

T2 - a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

AU - Zheng, Jie

AU - Rodriguez, Santi

AU - Laurin, Charles

AU - Baird, Denis

AU - Trela-Larsen, Lea

AU - Erzurumluoglu, Mesut

AU - Zheng, Yi

AU - White, Jon

AU - Giambartolomei, Claudia

AU - Zabaneh, Delilah

AU - Morris, Richard

AU - Kumari, Meena

AU - Casas, Juan-Pablo

AU - Hingorani, Aroon D

AU - Evans, David

AU - Gaunt, Tom

AU - Day, Ian

PY - 2017/1

Y1 - 2017/1

N2 - Motivation: Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r2) of the variants. However, haplotypes rather than pairwise r2, are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this paper, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.Results: Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits (GIANT) height data, HAPRAP performs well with a small training sample size (N<2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by SNPs with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).Availability: The HAPRAP package and documentation are available online: http://apps.biocompute.org.uk/haprap/

AB - Motivation: Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients (r2) of the variants. However, haplotypes rather than pairwise r2, are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this paper, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.Results: Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits (GIANT) height data, HAPRAP performs well with a small training sample size (N<2000) while other methods become suboptimal. Moreover, HAPRAP’s performance is not affected substantially by SNPs with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).Availability: The HAPRAP package and documentation are available online: http://apps.biocompute.org.uk/haprap/

U2 - 10.1093/bioinformatics/btw565

DO - 10.1093/bioinformatics/btw565

M3 - Article

VL - 33

SP - 79

EP - 86

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 1

ER -